A digital librarian for all

In the last week of June, Suklaa organised The London Festival of Learning, on behalf of the UCL Institute of Education and in association with ISLS, IAIED, ACM and the Knowledge Lab. This festival offered a unique opportunity to bring together world experts in artificial intelligence, the learning sciences and technical innovations in education.

Bibblio got the opportunity to showcase what we do for learning and development, on a festival day which was themed "Bringing AI to Life". Read on to find out what Mads had to say (edited for readability):

---

What’s a Bibblio?

"I'm one of the founders of Bibblio. Although we're not an edtech platform at our core, we work with a number of them, and we’ve built a piece of software that can be used by really any kind of content platform. Recommender systems are at the heart of what we do: trying to match the right content to the right user. In education, recommendation has recently been lumped under the label of ‘adaptive learning’, and as well as helping you understand what Bibblio is I'll explain a bit about the differences and similarities between us and what adaptive learning companies do.

A million to one?

"We now live in a world where there are millions and millions of pieces of content. Often a learner is trying to find information via a smartphone screen, and we can maybe show them three items at a time - maybe 10 things at a time – but not a million things at a time. So how do we filter this vast amount of information down to something that is digestible?

10 billion hours a week

"We’re going to look to some of the world’s biggest content platforms for inspiration. Every week, people around the world spend more than 10 billion hours on Facebook, Spotify, Netflix and YouTube. At the heart of all of these platforms sits a recommendation system: a system that suggests content to that user based on their past behavior, based on the general behaviour on the platform, and perhaps based on some of the metadata insights that they have on the platform. What does that mean for learning? Apart from the fact that that’s 10 billion hours not spent learning mostly.

Educating in a vacuum?

"The first challenge we have to consider is that education doesn't exist in isolation. Just because we have some of the kids's time in school doesn’t change the fact that we want to keep them interested in learning in the outside world. We’re beginning to see people enlisting technology to try and do that. One way of doing that is with ‘adaptive learning’.

Adapting to new realities

"What does that actually mean? Think of adaptive learning as a curriculum driven approach to recommendation. You're trying to get a child to a certain stage, or to pass a certain test or to get to a certain level of skill, and you're trying to optimize the content, the resources and the interventions to get them to that point most effectively.

Heading in the right direction

"However that's not the only type of learning that we do. At some point over the course of your school career, and especially beyond, you start to engage in self-directed learning. Even in school, not everything you do is about passing a test. A lot is, but hopefully not everything, and in the future less and less will be. In any kind of self-determined part of the learning journey, like research, recommender systems become very important. How do you find the right information?

Give me a place to search from…

"Counter-intuitively, one of the challenges with a tool like Google today is that it’s adapting to us. My girlfriend is experiencing the challenges this presents at the moment. She's retraining to become a developer, and trying to learn by yourself is really difficult using Google in the beginning, because Google doesn't think you're a developer. We did a comparison between her results and those of one of the guys on our team who's been a developer for 10 years. If they Google the same thing, they get very different results because Google doesn't think she's interested in stack overflow. Google really is a recommendation engine, but it suffers from the problem of over-personalization. Since 2009, Google hasn't had an objective search result. Even if you go in incognito and you're not logged in, they still look at 36 data points to figure out what to show you. So we need ways to avoid these ‘filter bubbles’ and reductive personalization.

Where Bibblio comes in

"So where Bibblio comes in is helping content platforms, course platforms, research platforms trying to match content to users more effectively. We know that recommendations improve discovery but they also improve things like motivation and loyalty. If I come to a platform and that platform's good at helping me find the right stuff, the likelihood is that I’ll come back to that platform again and that I’ll begin to develop a relationship with that platform. The opposite is also true: if I arrive and I have a 90s online experience and I can't find anything, what is the likelihood that I'll come back again? As I’ve said, the education system doesn't live in a vacuum from the rest of the world: people bring their expectations of technology and software into the classroom. So, if I'm used to having these great experiences on Netflix or Google etc., that's what I expect in the classroom.

What next?

"Once the user has found something how do we most effectively show them what else they could be doing? For example, “here are the most cited resources”, “here are the most used resources”, “here are the ones most related to what you are doing at the moment”. You need to be able give people maybe three or four relevant choices. To give a real life example, Bibblio operates on OpenLearn (the Open University online portal). So, when you reach the end of a page other content you might like is shown that’s driven by Bibblio. If we do our job really well, we're a bit like the referee in football: no-one will ever notice us.

Business goals

"OpenLearn's mission as part of the Open University is to extend the use of their educational resources to as many people as possible, and has two functions. It's both to publish all of those resources for the public, but also to recruit students to the Open University. So this goes back to the early stages of the learning journey: how do we actually create the motivation to become a learner? How does that work on OpenLearn? People tend to go from casually visiting the platform, to signing up to one of these very informal free courses, and then becoming a full time student at the Open University. In fact, that happens several thousand times a year. The societal benefits of someone taking that course of action and changing the trajectory of their life in this way is quite substantial. It also has a very very positive contribution to the Open University's business, but that's a whole other side of this story.

Why Bibblio?

"What Bibblio really offers is a combination of AI smarts and plug and play recommendations. You can drop a tag, a piece of code, onto the page and we go and read that page to understand what the content is about. We do a full natural language processing to connect those resources to each other and then you can build modules which recommend content to users. Machine learning algorithms power those recommendations to make sure they’re, relevant, popular, and, if you want, recent.

What would you recommend?

"So the next question is what exactly is a good recommendation? In short: it's not one thing. If you look at e.g. Netflix, they deploy about 130 different algorithms to figure out what to show to people. We don't have quite that many, but that's okay: they've been doing it since 2006. Even with all those algorithms, they can essentially be grouped into three areas.

"The first is content based (based on understanding the content). If I'm doing research and I've found an article about lakes in Africa, the likelihood is that I'm on a journey where I'm trying to find out more about that, and unsurprisingly, we've seen is that there's a really strong connection between search and recommendation. What we see on platforms like Student Room etc. is that someone will come in search for 'biology' then find some search results, come back about six minutes later and search for 'mitochondria', because now they've realized that actually biology is this massive term, and then they come back maybe even a third or a fourth time and refine their search. So one of the really fascinating things about learning, quoting Donna Martins, is that search is great if you know what you're looking for. But the problem in learning is often we don't actually know what we're looking for yet and that's where recommendation comes in.

A digital librarian?

"The name Bibblio comes from bibliotheque, the library, and the idea of the librarian is really important. In the classic scenario, the librarian is the person who helps guide you to what you might not know yet, but on these digital platforms that we can operate 24 hours a day, who's the librarian? So that's really where recommender systems sit in the world: think of us like a digital librarian trying to help people find the right thing. Just like a librarian knows you personally, the second group of algorithms are behavior based. The reality is that some learning resources are better than others, and through looking at user behavior we can begin to identify the resources that seem to do a good job at a certain point in the journey. If students land there do they spend a significant amount of time? Do they leave again really quickly? Those are some of the signals that we can pick up and respond to. We can also monitor what you’ve already read or consumed: there's no reason to suggest that to you again.

Hidden patterns

"But it also gets more advanced, because we can begin to see your patterns of behavior. How are you similar to other people? One of the really fascinating ideas that I have in this space that we haven't actually been able to deploy yet is that you can also recommend people as well as content. So what if we could identify students and say “Actually you've read all this stuff, and you've read all that stuff, but you guys haven't really read much of the same so maybe you should talk to each other?” It's really a very open use case which is both good but also sometimes challenging for us. Personalisation is certainly a third element of this, but it also opens up discussions about filter bubbles and are we showing what people what they already know etc.?

Popping bubbles

"That is a bit less of an issue with the Bibblio platform, because we haven't built the platform to optimize for ‘shallow’ engagement. We're not necessarily going to be a great investment for the Daily Mail to drive more page views on their Kim Kardashian articles. But, we know that we can lift the engagement that we see around content resources. Whether it's teachers looking for resources on the National Geographic Education or Show My Homework, where we also feature, or students looking for materials like OpenLearn or Of Course, we actually know that by serving good recommendations to people we can improve the outcomes of that experience. At the heart of all this are things that don’t get mentioned too often in education these days: things like student motivation and teacher motivation.

Experience wins

"If I have a great experience with software I tend to want to use that software more, and that's really one of the challenges we have about getting educational technology adopted into the classroom. We need to make that software is a good experience for both students and teachers. That's always the feedback I've heard from the classrooms: if I have to spend 12 minutes logging in and then loading something up on all these screens, it's not going to work. So I think that's the other thing that's really important in this: that it plugs in and it helps deliver a better experience in real time. That’s the future that we hope the Bibblio will contribute to – one where anyone can learn easily in or out of the classroom using technology that loses nothing in comparison to the best content companies out there. And that’s all that we have for you today. I hope it was an insightful little view into the world of recommender systems! Thanks very much for listening."